Data Gravity and What It Means for Enterprise Data Analytics and AI Architectures

As data gravity grows rapidly, enterprises need to bring AI and analytics platforms and applications to the data.

Enterprise Data Analytics

Enterprise Data Analytics- New data-intensive applications like data analytics, artificial intelligence and the Internet of things are driving huge growth in enterprise data. With this growth comes a new set of IT architectural considerations that revolve around the concept of data gravity. In this post, I will take a high-level look at data gravity and what it means for your enterprise IT architecture, particularly as you prepare to deploy data-intensive AI and deep learning applications.

What is data gravity?

Data gravity is a metaphor introduced into the IT lexicon by a software engineer named Dave McCrory in a 2010 blog post.1 The idea is that data and applications are attracted to each other, similar to the attraction between objects that is explained by the Law of Gravity. In the current Enterprise Data Analytics context, as datasets grow larger and larger, they become harder and harder to move. So, the data stays put. It’s the gravity — and other things that are attracted to the data, like applications and processing power — that moves to where the data resides.

Why should enterprises pay attention to data gravity?

Digital transformation within enterprises — including IT transformation, mobile devices and Internet of things — is creating enormous volumes of data that are all but unmanageable with conventional approaches to analytics. Typically, data analytics platforms and applications live in their own hardware + software stacks, and the data they use resides in direct-attached storage (DAS). Analytics platforms — such as Splunk, Hadoop and TensorFlow — like to own the data. So, data migration becomes a precursor to running analytics.

As enterprises mature in their data analytics practices, this approach becomes unwieldy. When you have massive amounts of data in different enterprises storage systems, it can be difficult, costly and risky to move that data to your analytics clusters. These barriers become even higher if you want to run analytics in the cloud on data stored in the enterprise, or vice-versa.

These new realities for a world of ever-expanding data sets point to the need to design enterprise IT architectures in a manner that reflects the reality of data gravity.

How do you get around data gravity?

A first step is to design your architecture around a scale-out network-attached storage (NAS) platform that enables data consolidation. This platform should support a wide range of traditional and next-generation workloads and applications that previously used different types of storage. With this platform in place, you are positioned to manage your data in one place and bring the applications and processing power to the data.

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Article Credit: CIO

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